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In 2016, Spanish banking group BBVA offered to its Spain-based customers a personal finance management app. One of the app’s tools used machine learning algorithms to sort customer transactions into common budgeting categories such as rent, food, and entertainment, and then it displayed a customer’s expenditures broken down as a simple chart. BBVA promoted the categorizer on its digital banking website as a way for customers to better manage their personal budgets. In just a year and a half, the tool became the most utilized feature on the BBVA website, second only to funds transfer.
And in 2019, the global consumer packaged-goods company PepsiCo formally launched a suite of data analytics capabilities called Pep Worx that supported a variety of use cases — such as how to successfully launch and manage innovative marketing programs and how to optimize total store space — that helped retail customers increase product turns, profit realization, and net price realization (the latter because there was less of a need to discount products that weren’t selling). PepsiCo developed the capabilities over a four-year period as the company solved problems for select retail customers using data analytics-based shopper insights. PepsiCo has used Pep Worx to help transform the nature of its retail customer relationships from transactional to collaborative by creating a “three-audience win” whereby sales or marketing activities simultaneously deliver value for the shopper, the retailer, and PepsiCo.
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These examples demonstrate how companies benefit from an emergent approach to data monetization we refer to as data wrapping. With this approach, a company’s products are “wrapped” in data analytics features and experiences that help and delight customers, with profitable results. The tendency for most companies is to draw upon preexisting business intelligence groups, data platforms, and analytics talent for data wrapping. However, the capabilities, processes, and skills that historically helped the company use data analytics to do things better, cheaper, and faster are insufficient for producing data analytics that delight customers.
1.The 2018 MIT CISR Data Monetization Survey was administered to executives familiar with the company’s enterprise-level data activities and outcomes. The executives were asked to estimate the percentage that each data monetization activity (improving, wrapping, and selling) contributes to the company’s total returns from data — to total 100%. The average break-out across the 315 responses for data wrapping was 26%.